• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.587-609
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• 2019

Short-and long-term stabilities of bentonite, favored material as buffer in geological repositories for high-level waste were reviewed in this paper in addition to alternative design concepts of buffer to mitigate the thermal load from decay heat of SF (Spent Fuel) and further increase the disposal efficiency. It is generally reported that the irreversible changes in structure, hydraulic behavior, and swelling capacity are produced due to temperature increase and vapor flow between $150{\sim}250^{\circ}C$. Provided that the maximum temperature of bentonite is less than $150^{\circ}C$, however, the effects of temperature on the material, structural, and mineralogical stability seems to be minor. The maximum temperature in disposal system will constrain and determine the amount of waste to be disposed per unit area and be regarded as an important design parameter influencing the availability of disposal site. Thus, it is necessary to identify the effects of high temperature on the performance of buffer and allow for the thermal constraint greater than $100^{\circ}C$. In addition, the development of high-performance EBS (Engineered Barrier System) such as composite bentonite buffer mixed with graphite or silica and multi-layered buffer (i.e., highly thermal-conductive layer or insulating layer) should be taken into account to enhance the disposal efficiency in parallel with the development of multilayer repository. This will contribute to increase of reliability and securing the acceptance of the people with regard to a high-level waste disposal.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.